#导入
import tensorflow as tf

#加载数据
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()


#数据预处理   数据要根据数据集和模型进行预处理

x_train, x_test = x_train/255, x_test/255


#构建模型
model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28,28)),
    tf.keras.layers.Dense(128,activation='relu')
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10,activation='softmax')
])


model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)

#模型训练

model.fit(x_train,y_train,epochs=5)

#模型测试

model.evaluate(x_test, y_test)


#模型预测
model.predict()










